Manual Curation Tool for Map Data Using Aggregated Overhead Views

ABSTRACT

Examples disclosed herein may involve (i) obtaining a first layer of map data associated with sensor data capturing a geographical area, the first layer of map data comprising an aggregated overhead-view image of the geographical area, where the aggregated overhead-view image is generated from aggregated pixel values from a plurality of images associated with the geographical area, (ii) obtaining a second layer of map data, the second layer of map data comprising label data for the geographical area derived from the aggregated overhead-view image of the geographical area, and (iii) causing the first layer of map data and the second layer of map data to be presented to a user for curation of the label data.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application hereby incorporates by reference U.S. patentapplication Ser. No. 16/731,902, which was filed on Dec. 31, 2019 and isentitled “Overhead View Image Generation.”

BACKGROUND

Satellite imagery and aerial photography both provide a view of theearth from above, and both can be used in geographical studies such asto survey areas of land. While both processes can produce digitalimages, satellite images have greater large-scale scientificapplications, and aerial photography has greater small-scale commercialapplications.

Aerial images are typically generated using manual photography and donot provide orthographic or overhead views. Rather, they provide avariety of elevated and perspective views from a variety of overheadviewpoints of a geographic area as the aerial platform from which theimages are captured passes over that geographic area. As a result,satellite imagery has more often been used for mapping, environmentalmonitoring, and archaeological surveys using satellites whichcontinuously orbit the earth. Although satellites can provide greatercoverage of the earth it comes at a high logistical cost. Further,satellite images of the earth's surface can be geometrically distorteddue to camera lens' properties or undesirable movement of thesatellites. This can provide inaccurate images of the real-world whichoften hinders their application for mapping purposes. Nowadays, althoughaerial photography can be more cost effective and be kept more up todate compared to satellite imagery, the image quality is also subject tovarious environmental factors which can hinder their use for mappingapplications. Additionally, the images gathered typically give no orlimited information on the surface elevation of a geographic area.

As satellite and aerial images are obtained from imaging devices at asignificant distances above the earth's surface, objects may occludewhat is visible at the imaging devices and so the images collectedusually include a number of occlusions in the view captured of theearth's surface. Additionally, typically images are not captured at asufficiently high resolution to be used to extract or generate mappingdata such as map semantics and/or map features.

Current overhead-view maps that are generated using aerial photography,satellite imagery or cartography do not, however, typically generatesufficiently sharp images that can be used for applications that requirehigh precision, such as for example with autonomous vehicles.

Moreover, existing techniques for manually or semi-automaticallyrefining map data are time consuming, usually taking curators hours tomanually clean and verify a given area of a map, as curators aretypically only presented with limited field-of-view images in order toverify semantic map data. Thus, the data provided to the curators istypically not suitable for efficient or effective manual curation orannotation, and thus can lack scalability for larger geographical areasor large-scale map data.

SUMMARY

In one aspect, the disclosed technology may take the form of a firstmethod that involves (i) obtaining a first layer of map data associatedwith sensor data capturing a geographical area, the first layer of mapdata comprising an aggregated overhead-view image of the geographicalarea, where the aggregated overhead-view image is generated fromaggregated pixel values from a plurality of images associated with thegeographical area, (ii) obtaining a second layer of map data, the secondlayer of map data comprising label data for the geographical areaderived from the aggregated overhead-view image of the geographicalarea, and (iii) causing the first layer of map data and the second layerof map data to be presented to a user for curation of the label data.

In example embodiments, the first method may further involve generatingthe aggregated overhead-view image of the geographical area using aground map of the geographical area and a plurality of images of thegeographical area. In these example embodiments, the function ofgenerating the aggregated overhead-view image further may involvedetermining a color for each of the aggregated pixel values.

Still further, in example embodiments, curation of the label data maycomprise one or more of: verifying the label data, editing the labeldata, adding new label data to the label data, or removing incorrect orirrelevant label data from the label data based on the correspondence ofthe aggregated overhead-view image to the label data.

Further yet, in example embodiments, the second layer of the map datamay be generated by automatically extracting label data from theaggregated overhead-view image of the geographical area, where the labeldata comprises semantic map data.

Further, in example embodiments, the label data may be automaticallygenerated using one or more of: machine learning models; classifiers; orGenerative Adversarial Networks.

Still further, in example embodiments, the label data comprises one ormore of: lane boundaries; lane connectivity; speed limits; types oftraffic elements; crosswalks; speed bumps; pedestrian paths orsidewalks; manhole covers; or curbs.

In another aspect, the disclosed technology may take the form of asecond method that involves (i) receiving labeled map data of ageographical area from a mapping system, the labeled map data including:(a) a first layer of map data comprising an aggregated overhead-viewimage of a geographical area, where the aggregated overhead-view imageis generated from aggregated pixel values from a plurality of images ofthe geographical area, and (b) a second layer of map data comprisinglabel data for the geographical area derived from the aggregatedoverhead-view image of the geographical area, (ii) displaying the firstlayer of map data and the second layer of map data to a user, (iii)receiving user input comprising one or more adjustments to the labeldata, and (iv) causing the label data to be adjusted in accordance withthe user input.

In example embodiments, the function of causing the label data to beadjusted in accordance with the user input may comprise one of (i)adjusting the label data locally and then providing the adjusted labeldata to the mapping system or (ii) providing the user input to themapping system and thereby causing mapping system to adjust the labeldata.

Further, in example embodiments, the function of causing the label datato be adjusted in accordance with the user input may comprise causingthe second layer of map data to be updated.

Still further, in example embodiments, the second method may furtherinvolve updating the displayed second layer of the map in accordancewith the user input.

Further yet, in example embodiments, the one or more adjustments to thelabel data may be based on one or more of: a set of guidelines; a set ofinstructions; one or more plug-ins for adjustment; or one or more toolsfor adjustment input.

Further, in example embodiments, the one or more adjustments of thelabel data may comprise one or more of: visual manipulation; determiningabnormalities; determining alignments/misalignments; inputting one ormore annotations; selecting/de-selecting one or more of the label data;removing/re-embedding one or more of the label data; hiding/exposing oneor more of the label data; or enlargement/diminution of one or more ofthe label data.

Still further, in example embodiments, the map layers may be stored inone or more local system or a remote system.

Further yet, in example embodiments, the second method may furtherinvolve causing a global map to be updated in accordance with the userinput.

Further, in example embodiments, the function of displaying the firstlayer of map data and the second layer of map data may comprisedisplaying the second layer of the map overlaid on the first layer ofthe map.

In yet another aspect, the disclosed technology may take the form of acomputer system comprising at least one processor, a non-transitorycomputer-readable medium, and program instructions stored on thenon-transitory computer-readable medium that are executable by the atleast one processor such that the computer system is configured to carryout the functions of the aforementioned first and/or second method.

In still yet another aspect, the disclosed technology may take the formof a non-transitory computer-readable medium comprising programinstructions stored thereon that are executable to cause a computersystem to carry out the functions of the aforementioned first and/orsecond method.

It should be appreciated that many other features, applications,embodiments, and variations of the disclosed technology will be apparentfrom the accompanying drawings and from the following detaileddescription. Additional and alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments will now be described, by way of example only and withreference to the accompanying drawings having like-reference numerals,in which:

FIG. 1 illustrates an automatically aligned and labelled semantic layeron top of a single field-of-view image where the semantic layer containsincorrect semantic label data points;

FIG. 2 illustrates corrected semantic label data overlaid on the singlefield-of-view image of FIG. 1;

FIG. 3 shows an aggregated overhead-view image generated using groundmap data and image data collected at substantially ground level;

FIG. 4 illustrates an automatically aligned and labelled semantic layeron top of an overhead-view image layer where the semantic layer containsincorrect semantic label data points; and

FIG. 5 illustrates corrected semantic label data overlaid on theoverhead-view image of FIG. 4;

FIG. 6 shows a flowchart for use with generated overhead-view imagesshowing the steps of manual curation of map data and semantic labelling;

FIG. 7 illustrates an overview of the user interface tool used and thedistribution of tasks to manual curators; and

FIG. 8 illustrates an example of a typical computer system or computingdevice that can be used to execute the described processes of theclaimed invention.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

FIG. 1 shows a traditional method of map data curation which is carriedout on an individual image having a limited field of view/perspective,which can be an image taken from a sequence of images captured for ageographical area. It will be appreciated that the term limitedfield-of-view images refers to images typically captured by one or moreimage sensors which are mounted on or within the vehicle (e.g., insequence along a trajectory travelled by a vehicle). For example, thiscould be a frame of a video captured from a single camera that is facingforward (i.e. in the direction of forward travel of the vehicle) andmounted within the vehicle and which captures a sequence of image framesas a video as the vehicle traverses the geographical area.

FIG. 1 shows an example of an original image frame 101 (from thesequence of image frames) with an initial layer of semantic data 102overlaid for a human curator to review and verify, edit or adjust usinguser-editable points 103. FIG. 1 also illustrates some possible errorsin the semantic data 102, such as parts of the road 104 that notcorrectly identified as a lane and incorrect lane estimations 105, whichlead to misaligned lane semantics where the lines of the layer ofsemantic data 102 clearly do not align with the lane markings of theoriginal image frame 101. The image also depicts real-world objects suchas other vehicles (and their shadows) 106 as well as nature 107 besidethe road that can interfere with the images captured, or interfere withthe curator being able to properly identify semantic features on theimages and correct semantic data (e.g., by adding or modifying asemantic label). For example, such images may work well for verifyingthe semantic data for lanes on which the vehicle (from which thesequence of images was captured on) travelled, but this approach canresult in limited accuracy when a curator attempts to correct semanticdata for neighboring lanes, for example due to the three-dimensionalperspective views being represented by a two-dimensional image.

Typically, large portions of a map, or sometimes an entire map, aremanually reviewed and verified by a human curator (who may alsosometimes be referred to as an annotator or a reviewer). Existingtechniques of manually or semi-automatically refining map data, andparticularly map label data, are time consuming, usually taking humancurators hours to manually clean and verify a given area of a map.Traditional map refining tools provide human curators with limited,single field-of-view images captured by sensors from a vehicle that arecorrelated with geometric map information associated with the capturedmap area to allow the curators to manually correct, guide, or refinegeographic and/or semantic map features or map data. However, thelimited field-of-view images can be difficult to use to get a full viewof the relationship of the map features in the image to the geometricmap information and the single field of view that is currently providedcan distort the geometric information, leading to difficult orinaccurate editing/curation of the map information.

FIG. 2 shows a manually-curated version of the field-of-view image andthe overlaid layer of semantic data previously shown in FIG. 1. Inparticular, FIG. 2 illustrates an alignment of the semantic layer 202 tothe image following manual curation of the semantic data associated withthe image. The curator can correctly align the semantic layer, forexample by adjusting the semantic data to correctly represent the lanearea 204 in the layer of semantic data or correctly aligning the edgesof the lanes 205 in the layer of semantic data to the lane markings onthe original image using user-editable points 103. However, conventionalmanual review processes of field-of-view images and correspondingsemantic label data typically lack scalability or precision.

Manual curation using these limited field-of-view images can be timeconsuming because there are many images for a geographic area that needto be reviewed in sequence in order to check the semantic data iscorrect, and the manual curation process also requires complicatedlabelling policy documentation to mitigate the limitations of the imagesused. For example, to illustrated with reference to FIG. 2, the curatormay be restricted (by policy documents or the user interface system) toonly edit semantic data in the bottom third 206 of the limitedfield-of-view image as any visible information above the bottom third ofthe image is likely to fall beyond a distance threshold or limitation asaccuracy of the visual data decreases with distance from the imagesensor(s) in single field-of-view images. Further, as mentioned above,limited field-of-view images often have objects or obstructions present,shadows, and other superfluous information contained in them that canmake it difficult to accurately curate map information. Further yet,single field-of-view images are restricted to limited perspective viewsand angles, and other geometric image distortions. These problems canlead to restrictions on the use of such images for editing and mayrequire additional image captures, long edit times and/or editingprocesses, multiple passes over the same region by multiple differentcurators, and/or highly trained curators. Still further, such limitedfield-of-view images being used to train semi-automatic or automaticcuration processes would be computationally costly as many images needto be processed for a given geographical area.

Referring to FIGS. 3 to 8, example embodiments relating to a method ofusing aggregated overhead-view images of a geographical area for mapdata curation will now be described. Example embodiments use rich,contextual, aggregated overhead-view images (generated from sequences ofmultiple substantially ground level field-of-view images) to provideusers such as curators with top-down views of a geographical area,street segment, and/or other features. In this way, such aggregatedoverhead-view images may provide rich, contextual, and unobstructedimages to assist a curator with manual curation. The example embodimentsenable map labelling to be performed using rich top-down views ratherthan limited field-of-view camera images. Once a manual curation taskperformed on an image such as this is verified, complete, and submitted,the results can be saved or stored in a map database. Additionally, onceimages have been verified and submitted following verification, theimages can be stored for further distribution for quality assurance orbe used as training data for an automated data-curation system.

Example embodiments describe map data curation using aggregatedoverhead-view images that have been generated using ground map data incombination with image data, which may involve aggregating pixel valuesfrom a plurality of source images of the geographical area. This type ofaggregated overhead-view image can provide various advantages such asgenerating images that accurately align with simultaneous localizationand mapping (SLAM) techniques. The example embodiments can also generatefaster and more efficient image updates compared to aerial photographytechniques due to the use of ground vehicles or substantiallyat-ground-level image sensors rather than aerial imagery, and can alsogenerate images which do not include unwanted features that can occludethe drivable surface or the ground area as observed from above.

Overhead view images seek to represent the real-world surface ofgeographical areas. Although the images of the area/environment can becaptured using vehicles equipped with image sensors or image sensorarrays, the raw images may also be obtained by an image sensor in anyform, for example, a smartphone or a digital camera. The image data canalso be accompanied by image metadata, including but not limited to,timestamp information, position and orientation (pose) data, InertialMeasurement Unit (IMU) data, and other corresponding sensory data, whichcan be transmitted to a network and/or other computer systems. Imagedata can be obtained as sequential image data or data collected over oneor more trajectories, however, not every image collected is necessary oruseful for overhead-view image generation due to occlusions, forexample.

FIG. 3 shows an aggregated overhead-view image, generated in an exampleembodiment. This illustration shows a colored drivable surface 302excluding the segmented areas 304 where it has been determined thatthere is no drivable surface. Particularly, example embodiments seek togenerate content rich aggregated overhead-view images of geographicalareas on top of ground map data, the ground map data providing arepresentation of the surface topology of a geographical area, usinglimited field-of-view images captured from a substantially ground levelperspective. This results in higher-resolution overhead-view imagesbeing generated without, or with substantially fewer, occlusionscompared to using satellite or other aerially-captured images.Specifically, FIG. 3 shows a generated or aggregated overhead-view imageof the ground surface 302 which clearly includes all the road markingsand significantly less interference caused by trees, street furniture,and other occlusions.

In generating an aggregated overhead-view image, the ground area of thefield-of-view images used for overhead-view image generation can bedivided into two-dimensional sections or points, described herein assampling points, cells, pixels or patches. Each sampling point isassociated with a portion of the point cloud data of the ground map.Optionally, or additionally, the raw image or the ground map can bedivided into sections of the geographic area based on a globalpositioning system (GPS) or other coordinate scale. Dividing the groundmap into a plurality of sampling points comprises dividing the groundmap into any one or any combination of: square grids, tiles, and/orquadtrees and/or hierarchical spatial data structures. As it would beknown to a skilled person, in order to support large ground areas thatmight not fit into one single generated overhead-view image, the groundarea can be represented by smaller images, divided by sub regions orinto small sections, on top of which a quadtree is built for fastnavigation and small memory footprint.

In some embodiments, the ground map can be queried in order to determinea three-dimensional position of all of the visible sections of theenvironment to be mapped in each image of the environment. In exampleembodiments, in order to determine which images capture a view of thesame scene or location, intersection rays are extrapolated and computedfor each image from each respectable camera's origin to the ground mappoint. Back-propagation of intersection rays determines the relevantportion of each camera image.

In embodiments, the selection of which of the input images aredetermined to represent each sampling point on the ground area will nowbe described. The image-view rays for each image can be computed fromthe ground map sampling point to each of the image field-of-view points.As the vehicle traverses along a trajectory, only certain sample pointswill be visible at certain positions of the imaging device and thus canbe used to determine the color of the sampling point in the generatedoverhead-view image.

In example embodiments, the color of each of the patches of theaggregated overhead-view image can be determined by determining acorrelation between the sampling points of the ground area and the colorof each sampling point captured by one or more of the plurality ofimages including a view of the respective sampling point. For example,the correlation can be a determined two-dimensional coordinate in animage (or alternatively a determined area of an image) corresponding toa three-dimensional coordinate of the ground map. In this way, eachsampling point of the ground area is queried against all images in whicheach point of the ground map is visible and relevant pixels in theimages are aggregated to determine a color for each sampling point,which is used as the color of the corresponding patch of the aggregatedoverhead-view image. Although a color can be determined for each patchbased on one camera view image, in example embodiments the color isdetermined for each of the sampling points from the correlated areas ofmultiple images. Optionally, the color can be determined for at leastone pixel of a portion of a camera image that is not masked out by anysegmentation. However, in some embodiments, the color for each of thesampling points can be determined from the correlating areas of multipleimages, and more specifically from the relevant pixels of portions ofcamera images that are not masked through segmentation. In someembodiments, a median or appropriate algorithm may be used to determinethe color for each patch.

Although an aggregated overhead-view image of an area can be generatedusing images obtained over a single trajectory, for example, this maynot generate a complete overhead-view image due to gaps where there areocclusions between the camera sensor and some of the sampling points ofthe ground area. Therefore, in some embodiments, it can becomputationally efficient to load or generate the aggregatedoverhead-view image only when sufficient data is obtained for eachsection of the map (e.g., data from multiple trajectories over ageographical area that is obtained from one or more vehicles) in orderto reduce costly iterative computation and processing to recreate largeand dense overhead-view images when new data is collected to fill inmissing portions of generated overhead-view images. For instance, insome embodiments, five to ten data collects (i.e. sequences of imagesgathered along a trajectory), or data streams can be used. In otherembodiments, a dataset of thirty to fifty images can be used todetermine the average or median value of a patch color. In exampleembodiments, all camera images that potentially include within theirfield of view each sampling point are determined for each samplingpoint. Thus, in example embodiments, the quality of color representationcan be improved with more collects at the same location or of the samegeographic area. In some embodiments, typically five to ten datacollects, or data streams are collected.

In example embodiments, the exact field-of-view ray, or image rays, foreach image to sampling point is computed so that each sampling point canbe associated with each image collected that includes that samplingpoint in its field of view. For each sampling point, the color at thecorresponding intersection with the image plane is determined. In thisway, a list of color values can be determined and stored for each imagethat views the sampling point which can be used to determine the finaloutput color for the generated overhead-view image once sufficient datais collected.

More detail about generating overhead-view images suitable for manualcuration is described in further detail in U.S. patent application Ser.No. 16/731,902 entitled “Overhead View Image Generation,” which as notedabove is incorporated herein by reference in its entirety.

In some embodiments, it is possible that aggregated overhead-view imagesgenerated in the manner described above could include errors, examplesof which include duplicated structures, blurriness, distortion, shadows,and/or errant artifacts (e.g., semi-circular artifacts caused bypresence of a vehicle roof/hood), among other possibilities. In thisrespect, certain errors corrections may be applied in order to improvethe quality of the aggregated overhead-view images, examples of whichmay include a modification to one or more of the parameters related tothe field-of-view image data used during generation of overhead-viewimages, a modification to one or more of the parameters related to pointcloud data used during generation of overhead-view images, amodification to one or more of the parameters related to calibrationand/or synchronization of different sensor units used during generationof overhead-view images, a modification to one or more of the parametersrelated to generation of SLAM information used during generation ofoverhead-view images, and/or a modification to one or more of theparameters related to certain processing techniques (e.g., segmentation,projection, filtering, augmentation, etc.) that are utilized duringgeneration of overhead-view images, among other possibilities.

In some embodiments, an aggregated overhead-view image can be very largeand therefore it may not be possible to be stored as a single imagefile. In order to support large areas that might not fit into one singleimage, the aggregated overhead-view image can be divided into smallerimages by sub regions or small sections for more efficient data storageand processing.

Further, in some embodiments, an aggregated overhead-view image can bestored as part of a multi-layer map, which may be comprised of multiplelayers of map data having a common reference frame/coordinate system.For instance, in example embodiments, such a multi-layer map include,but not be limited to, a geometric map layer and a semantic map layer(along with any other layers that may be appropriate to capture relevantinformation for an area). In this respect, the geometric map layer mayinclude a ground map and/or an aggregated overhead-view image. In turn,the semantic map layer may include any information about an area thatmay help a user identify and describe the relationships and actions thatare appropriate for the area. For example, the semantic map layer mayinclude lane geometries, lane connectivity, identification of trafficelements (e.g., traffic lights, traffic signs, etc.), street elements(e.g., cross-walks, etc.), and any other relevant information. Much ofthis information may be extracted from sensor data collected in theregion. However, as described above, it can be difficult to accuratelyextract this information from single images that have imperfect orsubjective points of view at an accuracy level that may be useful forvehicles and/or other robotics platforms to rely upon. For example, lanegeometry data can be more efficiently extracted from the highlycontextual overhead-view image data that is generated from a variety ofdifferent collections and/or captures of the area across differenttimes, conditions, and positions.

In example embodiments, improving the quality of maps using techniquesto validate and/or align data can be used to recreate or update suchmaps. Map features and map information can be extracted to edit, improveand create layers of maps which can include, but are not limited to ageometric map (which may comprise a ground map) and/or a semantic map.Using a content rich aggregated overhead-view image can also enableother map semantics to be extracted or improved or annotated moreefficiently and with more accuracy compared to using field-of-viewimages. However, in some embodiments, it may be also possible to extractinformation to edit semantic information from the field-of-view image.

The geometric and semantic map layers can provide information about thestatic and physical parts of an environment that are important to, forexample, autonomous and semi-autonomous vehicles. These map layers canbe built at a very high fidelity and high precision about what theground truth is. In example embodiments, the map is viewed as acomponent that not only captures an understanding of the physical andstatic parts of the world, but also dynamic and behavioral aspects ofthe environment.

In some embodiments, the semantic map layer may contain or be associatedwith a road network graph. The road network graph can represent the roadsegments and the interconnections for a geographical area, including forexample: how many lanes there are for each road segment; what directionof travel is permitted in each lane; and how each road segment isconnected to other road segments. The road network graph may alsorepresent the yield/right-of-way properties between road segments andneighboring lanes, so that autonomous vehicles (or semi-autonomousvehicles/driver assistant systems) are able to navigate/understandtransitions between road segments, lanes and operate safely atintersections or crosswalks/crossings when encountering vehicle orpedestrian traffic. These are complex properties that can change inrelationship to other layers: for example, the state of a traffic lightmay influence which lanes need to yield; or alternately some lanes canvary between being one-way or two-way depending on the time of day.Autonomous vehicles (or semi-autonomous vehicles/driver assistantsystems) may use the road network graph to determine a path from A to B,and detailed semantic map information can help the such vehiclesmitigate risk by for example understanding the connections andrelationships between different portions of an area the vehicle istraveling through and help to understand how other agents may behave inthe environment as well as what paths and relationships others mayexpect the vehicle to move through.

As with any large dataset there will undoubtedly be a percentage ofbroken or corrupt map or image data. Therefore, in some embodiments, newdata that corresponds to an area needs to be tested before beingincorporated or used to update parts of a map. In some cases, the newdata is only incorporated in the global map if it satisfies apredetermined quality threshold. As large amounts of data are gathered,the predetermined quality threshold can be relatively high.Overhead-view images can be flawed due to breaks or errors within thedata, a lack of data, or outliers in data obtained by image sensors usedto generate overhead-view images. Map layers can be misaligned fornumerous reasons such as timestamp error or the lack of calibration ofsensor data.

Map data cleaning is the manual process of visually inspecting maplayers and map data and labelling or editing thebroken/misaligned/incorrect areas, which can then be corrected to helpimprove the quality of the final map. In example embodiments, therefore,a pre-processed or pre-generated overhead-view map or overhead-viewimage is generated or received, and portions of the map or image areextracted and determined whether validation is required. If required,map data cleaning can be performed.

Example embodiments will now be described of manual curation/map datacleaning of a geographical area of a map that is based on anoverhead-view image. With the example embodiments presented herein, byusing an accurate and more complete overhead view of a geographical areait is easier for users to understand and avoid inefficient processing ofmultiple individual images as only one overhead-view image is requiredfor a geographical area (rather than a sequence of limited field-of-viewimages). These overhead views can be used to provide semanticinformation for the whole geographical area rather than being reviewedby stepping through a sequence of limited field-of-view images one at atime.

In example embodiments, errors may still arise within a map that isbased on an aggregated overhead-view image due to misalignments of thesemantic layer for example. For instance, FIG. 4 depicts a map of ageographical area that may be presented by a curation tool or the like.As shown in FIG. 4, the presented map includes an aggregatedoverhead-view image 400, three vehicle trajectory lines 410 a in eachlane and road markings 404. A vehicle equipped with an image sensor(s)405 is shown along its traversed trajectory line 401. Each trajectoryline is illustrated with points where the image sensor captures alimited field-of-view image that can be used in accordance with themethods described above. Further, as depicted in FIG. 4, a semanticlayer 402 of the map may include a portion of the pavement/sidewalk 402that is incorrectly classified as the road surface and as part of alane, and the lanes in the semantic layer 402 may not correctly align tothe lane markings shown in the overhead-view image. The severity ofthese errors may be a subjective but can be important depending on therequired precision of the map. Thus, it may not be entirely reliable toimplement a fully automated system and therefore a semi-automated systemwith guidance can be provided to data curators through a user platform.

FIG. 5 shows a corrected version of the map illustrated in FIG. 4 onceit has been reviewed and adjusted by a user such as a curator. Inparticular, FIG. 5 shows a more accurate alignment of the semantic layer502 within the map data. FIG. 5 shows the correction made by a user thatadjusts the alignment of the lane labels such that the sidewalk/pavementis longer considered to be a lane on the road 502 a. As described above,a human user may user editable points (similar to points 103 depicted inFIGS. 1 and 2) attached to, or embedded within, parts of the semanticlayer to correct any misalignment errors between the aspect of the maprepresented in the aggregated overhead-view image and the semantic data.Additionally, once the semantic data for the map or areas/sectionsthereof have been verified and submitted as complete followingverification, the images and semantic data can be stored for furtherdistribution for quality assurance or be used as training data for anautomated data curation system.

Optionally, in some embodiments, for assistance to the users, trajectoryline 401 of the vehicle 405 and/or lines 410 a of other vehicles mayprovide the ability to access and view the limited field-of-view images403 that were used to generate the aggregated overhead-view image. Insome cases, the human user may be provided with a corresponding limitedfield-of-view image(s) for each section of the geographical area to usefor further verification. Vehicle trajectories can provide guidance forcurators to edit, adjust or verify the drivable area (or other semanticsdata) of the map more accurately.

FIG. 6 shows an example process showing some example functions that maybe carried out to facilitate manual curation of a map that includes afirst layer comprising an aggregated overhead-view image of a geographicarea and a second layer comprising label data for the geographic area.In order to enhance the accuracy of the map, a user can adjust thesecond layer of data (e.g. drivable areas or other semantic labels) toconform more accurately with the aggregated overhead-view image. Inpractice, this example process may be carried out by a computer systemsuch as a cloud-based mapping system (which may take the form of thecomputer system 800 described below with reference to FIG. 8), which maybe communicatively coupled to a user platform or system (which couldalso be referred to as a task management system, a manual validationsystem, or the like) that can be accessed by a plurality of curators toverify and submit tasks. In some embodiments, once a task is verified,complete, and submitted, the results can be saved or stored, and thesame task can be accessed with a given uniform resource locator (URL)for example.

As shown in FIG. 6, the example process may begin at block 602 byobtaining an aggregated overhead-view image.

At block 604, based on the aggregated overhead-view image, a map thatthat includes a first layer comprising an aggregated overhead-view image(e.g., a geometric layer) and a second layer comprising label data maybe created (e.g., a semantic layer). In this respect, the function ofcreating the map may involve automatically extracting the label data(e.g., semantic map data) from the aggregated overhead-view image usingone or more of: machine learning models; classifiers; or GenerativeAdversarial Networks.

At block 606, a set of tasks or units to be completed with respect tothe map (e.g., tasks for map areas that require validation) may becreated based on variables such as approximate time to complete task,for example.

At block 608, the created set of tasks or units to be completed withrespect to the map may be output to a user platform (e.g., a curator'scomputer system), which may present the created set of tasks or units toa curator.

At block 610, the map may be refined based on user input received viathe user platform, where such user input may reflect reasoned orquality-based judgements, annotations, and/or visual manipulations ofthe section or area of the map. In this respect, the user input maycomprise a requested adjustment to label data included in the secondlayer of the map, and the function of refining the map may involve (i)refining the map data based on adjusted label data that we createdlocally by the user platform or (ii) adjusting label data at the mappingsystem based on user input received from the user platform.

In some embodiments, prior to presenting such maps or areas of such mapsfor manual verification, there can be provided a function of automatedprocessing of the map data in order to highlight high-confidence defectsto curators. For instance, in some embodiments, a checklist ofpredetermined errors can be used by an automated processing function todetermine if there are any errors in any portion of a semantic layer ofthe map data and these errors can be used to highlight high-confidencedefects to curators. In other embodiments, where conflicting semanticlabels are applied to regions of the map (for example to indicate thatan area of a map is both a drivable road and a non-drivable sidewalk orverge) then the automated processing function can highlight this as ahigh-confidence defect. In some embodiments, the processing used toproduce the semantic label data from the aggregated overhead-view imageis configured to output confidence values for each semantic feature thatis generated or determined from the overhead-view image and wheresemantic label data is output with a low confidence value (or aconfidence value below a predetermined threshold) then this can behighlighted as a high-confidence defect to the curators. In someembodiments, the automated processing function can further suggest oneor more corrections for each of the highlighted high-confidence defects(for example a new location of a semantic label can be recommended andthe difference between the label in the semantic layer and therecommended new location for the semantic label can be displayed to thedata curators). To assist the curators, for each semantic label orrecommended new location of a semantic label, the relevant originalimage(s) used to generate the overhead-view image can be shown alongsidethe overhead-view image to the data curator to enable the data curatorto validate or correct the semantic labels.

Turning to FIG. 7, an example of a pipeline for preparing map data to bechecked automatically/manually/semi-automatically is illustrated, whichmay enable allocated or prepared tasks to be performed by a curator orteam or curators. As shown in the example pipeline of FIG. 7, one ormore overhead-view images 702 included in a map may be input into a unitpreparation engine 704, which may prepare one or more units for curationand may then output the one or more units to a user platform 710 thatenable a curator to curate the overhead view images 712 (e.g., bymodifying, adding, or removing label data).

In example embodiments, the one or more units 708 can include more thanone section or area of the map and in some embodiments can be allocatedin accordance with the time it takes to verify the map data or based onthe contextual analysis of each section or area. For example, each unit(or “task”) prepared for validation/verification or judgment could be ofapproximately ten to fifteen-minute tasks, however it is not limited tothis and may for example be a shorter or longer task depending on thelevel of quality assurance a particular section or area of theoverhead-view image has previously been assessed for.

Further, in some embodiments, the one or more units 708 prepared formanual curation through the unit preparation engine 704 may be combined,collected or grouped together in various permutations. For computationcost efficiency, systems can group and prepare overhead-view images orareas/sections thereof together based on a variety of factors. In someembodiments, previously-verified overhead-view images of an overlappingarea can be prepared into a unit of overhead-view images orareas/sections thereof to enable the curator to make more accurate orbetter reasoned judgements based around context of the area. In exampleembodiments, the curator may be required to review and verify image datawithin the overhead-view image in order to recreate or update the globaloverhead-view map or sections of the global overhead-view map.

In example embodiments, the one or more units 708 or areas/sectionsthereof are presented to a curator based on co-geographical location orcontext for example. Example embodiments may also present units insubstantially computationally cost efficient or cost effective methoddepending on a variety of factors, such as for example the geographicallocation of a curator and/or the time set for tasks on each of thecurator's platform or system, or alternatively the curator may becapable of selecting tasks to be performed. In some embodiments, inorder to assist the process of manual curation, it can be useful tounderstand the mapped environment such as the vehicle path orfield-of-view images of the area in order to assess overhead-view imagesor areas/sections thereof more effectively based around context.

As noted above, the one or more units 708 may be output to a userplatform 710, otherwise known as a manual curation or validationplatform or system, which may then enable the curator to make judgmentsand verify semantic data for map areas represented by unit(s) 708. Acurator may check each representation of a geographical area includedwithin the unit and input one or more judgments such as annotations ofthe semantic layer of map data for the respective geographical area. Inexample embodiments, the user platform 710 can provide a map layerpresentation which can display to the curator a centered view of themap, a semantic layer and/or geometric layer of the map based on theoverhead-view image generated of the ground map or drivable surface.

As shown in FIG. 7, the verification of tasks is performed via the userplatform 710 to essentially visualize and allow a curator to validateand/or correct map data. As aforementioned, the system may includeautomated and/or semi-automated sematic curation steps (not included inFIG. 7). Curators may also be able to create annotated commentsregarding any aspect of the task being assessed. By way ofsemi-automatically validating or verifying overhead-view images orareas/sections thereof to be used to regenerate a global overhead-viewmap essentially solves the problems that arise from assumptions ofautomatic assessment and analysis.

In example embodiments, the user platform 710 can be provided using thecomputer system 800 shown in FIG. 8 and described in more detail below.

In example embodiments, the user platform 710 in FIG. 7 can include oneor more overhead-view image curation engines or software tools toperform the tasks associated with generating more accurate and preciseoverhead-view images, enabling visibility of labels and/or tags, whichmay be determined automatically or by means of manual input, to a useror a plurality of users. The user interface of the user platform 710 mayform part of a web platform and/or a browser extension which providesusers with the ability to manually label, tag and/or edit overhead-viewimage data. The user platform 710 may include a curation capture enginethat functions to capture and store manually curated data. In someembodiments, the user platform 710 may provide a curator with tools to,for example, rotate overhead-view images, highlight overhead-view imagesor areas/sections thereof, label overhead-view images or areas/sections,or visualize the impact of decisions or inputs. Inputs may include forexample, labelling map layers, labelling map data, labelling buildings,road names, and/or tourist attractions or landmarks.

The quality checking of overhead-view images or geographicalareas/sections is reliant on the accuracy of the curators, so it isvital to ensure the curators are as accurate as possible. Thus, bypresenting the semantic data on a generated overhead-view image, humancurators can review the map data and data points for an entiregeographical area in one view and the semantic data can be annotated,reviewed, and/or adjusted to align the various layers of theoverhead-view image map for an entire geographical area without needingto review a sequence of limited field-of-view images and correctsemantic data in each of the sequence of limited field-of-view images.In example embodiments, the curators can make edits or adjustmentsand/or manually label data by any one or any combination of: visualmanipulation; determining abnormalities; determiningalignments/misalignments; inputting one or more annotations;selecting/de-selecting one or more of the preliminary label data;removing/re-embedding one or more of the preliminary label data;hiding/exposing one or more of the preliminary label data; orenlargement/diminution of one or more of the preliminary label data. Inexample embodiments, the curator may also be capable of altering thedimensions of the adjustable semantic layer to determine a more accurateand precise overhead-view image.

The user interface presented to a curator by the user platform 710 maypresent a portion of the overhead-view image overlaid with the semanticdata for that portion of the overhead-view image. In addition, the userinterface can also display, at the curator's option using a toggle orswitch in the user interface controls, the one or more images originallycaptured for the relevant region of the overhead-view image that wereused to generate the overhead-view image—this enables the curator toverify, using a different point of view, whether the semantic data iscorrect or needs adjustment. Adjustment of the semantic data can beaccomplished through the user interface by using the various inputsdevices available to the curator, for example a mouse, touchpad, touchinterface, stylus and/or keyboard, to drag the labels to correctpositions or adjust bounding boxes or lines to adjust the dimensions ofthe semantic data relative to the overhead-view image and/or theoriginal point-of-view images. Missing semantic labels, for example forpedestrian crossings, lampposts, trees, sidewalks, stationery vehicles,segmented areas, drivable road surfaces, road lanes, roundabouts, lanemarkings, zones where specific traffic rules apply, etc. can be added.Similarly, existing semantic labels for these items can be adjusted ifmisplaced. Existing semantic labels that are left without amendment maybe assumed by the user platform 710 to be considered correct by thecurator.

As tasks are generated, in some embodiments a pool of tasks may beallocated to each of the curators or a plurality of data curators. Insome embodiments, for quality assurance purposes, once a task has beencompleted or a unit has been verified or annotated, the task may bepassed onto a second curator for further assessment and analysis/qualitychecking. Curators can review and verify existing overhead-view imagesor areas/sections thereof, newly generated overhead-view images orareas/sections thereof, and existing overhead-view images orareas/sections thereof which have already been through the verificationprocess. However, in some semi-automated processes, the computer systemor algorithm may take over in further assessing the overhead-view imagesor areas/sections thereof.

In example embodiments, there can be guidance provided via the userinterface of user platform 710 to help curators to understand thequality of overhead-view images or areas or sections of theoverhead-view images. The curator may be provided with guidelines or aset of instructions as reference for determining annotations, whatcauses overhead-view images or areas/sections thereof to misalign withcorresponding map layers for example, how to identify outliers anderrors etc. Guidelines/instructions for data curation can demonstrate tocurators, how overhead-view images or areas/sections thereof should beassessed, why overhead-view images contain errors or defects andexplanation of those errors, how to identify such errors, and how toinput and submit results. Such guidelines/instructions can provideconsistency to the curation process and can also be used to effectivelytrain new curators.

In some embodiments, the errors present in a displayed section of a mapand be determined and relevant guidance can then be shown to a curatordynamically. Alternatively, the most likely guidance for the currentview shown to a curator can be displayed. As a further alternative,based on user activity (for example, selecting a particular feature orfiltering for a particular type or types of semantic data), the userinterface can show the guidance determined to be the most relevant tothe current user operation. Through the user platform 408, the curatormay also be capable of identifying incorrect map data. In someembodiments, additional plug-ins may provide further tools to assistcurators manually verify each task assigned or presented via thecuration platform and can for example be used to apply shortcuts to thecuration platform for more efficient data curation.

In embodiments, the geometric map layer may contain three-dimensionalinformation of the world. This information can be very highly detailedto support precise calculations. Raw sensor data from LiDAR, variouscameras, GPS, and IMUs can be processed using SLAM algorithms to firstbuild a three-dimensional view of the region explored by the mappingdata collect (i.e. sequential collection of data from a vehicletraversing an environment to be mapped). The outputs of the SLAMalgorithm can be an aligned dense three-dimensional point cloud and avery precise trajectory taken by the mapping vehicle. Each of thethree-dimensional points can be colored using the colors observed forthat three-dimensional point in the corresponding camera images. Thethree-dimensional point cloud can be post-processed to produce derivedmap objects that are stored in the geometric map. Two important derivedobjects are the voxelized geometric maps and a ground map. The voxelizedgeometric map can be produced by segmenting the point cloud into voxelsthat are as small as 5 cm×5 cm×5 cm. During real-time operation, thegeometric map can be the most efficient way to access point cloudinformation. It can offer a good trade-off between accuracy and speed.Segmentation algorithms can identify three-dimensional points in thepoint cloud for building a model of the ground, defined as the drivablesurface part of the map. These ground points can be used to build aparametric model of the ground in small sections. The ground map can bekey for aligning the subsequent layers of the map, such as the semanticmap.

The semantic map layer can build on the geometric map layer by addingsemantic objects. Semantic objects can include various traffic, two- andthree-dimensional objects such as lane boundaries, intersections,crosswalks, parking spots, stop signs, traffic lights, etc. that areused for driving safely. These objects can contain rich metadataassociated with them, such as speed limits and turn restrictions forlanes. While the three-dimensional point cloud might contain all of thepixels and voxels that represent a traffic light, it is in the semanticmap layer that a clean three dimensional object identifying the threedimensional location and bounding box for the traffic light and itsvarious components can be stored. One can use a combination ofheuristics, computer vision, and point classification algorithms togenerate hypotheses for these semantic objects and their metadata. Theoutput of these algorithms might not be accurate enough to produce ahigh-fidelity map, however. Human operators can post-process thesehypotheses via rich visualization and annotation tools to both validatethe quality and fix any misses. For example, to identify traffic lights,one can first run a traffic light detector on the camera images. VisualSLAM can be used to process multiple camera images to get a coarselocation of the traffic light in three dimensions. LiDAR points in thelocal neighborhood of this location can be matched and processed toproduce the bounding box and orientation of the traffic light and itssub-components. One can also employ heuristics for solving simplerproblems. For example, heuristics can be useful in the generation oflane hypotheses, yield relationships, and connectivity graphs atintersections. There can be a lot of structure in how these are setupfor roads, especially since there are local laws that ensureconsistency. Feedback from the human curation and quality assurancesteps can be used to keep these up to date.

FIG. 8 illustrates an example computer system 800, which may beconfigured to perform the functions of one or more methods described orillustrated herein either alone or in combination with one or more othercomputer systems (which may take a similar form to computer system 800).In particular embodiments, software running on computer system 800 mayenable computer system 800 to perform one or more functions of the oneor more methods described or illustrated herein. Herein, a reference toa computer system may encompass a computing device, and vice versa,where appropriate. Moreover, a reference to a computer system mayencompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems800. This disclosure contemplates computer system 800 taking anysuitable physical form. As example and not by way of limitation,computer system 800 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, anaugmented/virtual reality device, or a combination of two or more ofthese. Where appropriate, computer system 800 may include one or morecomputer systems 800; be unitary or distributed; span multiplelocations; span multiple machines; span multiple data centers; or residein a cloud, which may include one or more cloud components in one ormore networks. Where appropriate, one or more computer systems 800 mayperform one or more functions of one or more methods described orillustrated herein without substantial spatial or temporal limitation.As an example, and not by way of limitation, one or more computersystems 800 may perform in real time or in batch mode one or morefunctions of one or more methods described or illustrated herein. One ormore computer systems 800 may perform one or more functions of one ormore methods described or illustrated herein at different times or atdifferent locations, where appropriate.

In particular embodiments, computer system 800 includes at least oneprocessor 802, non-transitory computer readable media such as memory 804and storage 806, an input/output (I/O) interface 808, a communicationinterface 810, and a bus 812. Although this disclosure describes andillustrates a particular computer system having a particular number ofparticular components in a particular arrangement, this disclosurecontemplates any suitable computer system having any suitable number ofany suitable components in any suitable arrangement.

In particular embodiments, processor 802 includes hardware for executingprogram instructions for causing computer system 900 to carry out one ormore functions of one or more methods described or illustrated herein.As an example, and not by way of limitation, to execute programinstructions, processor 802 may retrieve (or fetch) the instructionsfrom an internal register, an internal cache, memory 804, or storage806; decode and execute them; and then write one or more results to aninternal register, an internal cache, memory 804, or storage 806. Inparticular embodiments, processor 802 may include one or more internalcaches for data, instructions, or addresses. This disclosurecontemplates processor 802 including any suitable number of any suitableinternal caches, where appropriate. As an example, and not by way oflimitation, processor 802 may include one or more instruction caches,one or more data caches, and one or more translation lookaside buffers(TLBs). Instructions in the instruction caches may be copies ofinstructions in memory 804 or storage 806, and the instruction cachesmay speed up retrieval of those instructions by processor 802. Data inthe data caches may be copies of data in memory 804 or storage 806 thatare to be operated on by computer instructions; the results of previousinstructions executed by processor 802 that are accessible to subsequentinstructions or for writing to memory 804 or storage 806; or any othersuitable data. The data caches may speed up read or write operations byprocessor 802. The TLBs may speed up virtual-address translation forprocessor 802. In particular embodiments, processor 802 may include oneor more internal registers for data, instructions, or addresses. Thisdisclosure contemplates processor 802 including any suitable number ofany suitable internal registers, where appropriate. Where appropriate,processor 802 may include one or more arithmetic logic units (ALUs), bea multi-core processor, or may include multiple processing units.Although this disclosure describes and illustrates a particularprocessor, this disclosure contemplates any suitable processor.

In particular embodiments, memory 804 includes main memory for storinginstructions for processor 802 to execute or data for processor 802 tooperate on. As an example, and not by way of limitation, computer system800 may load instructions from storage 806 or another source (such asanother computer system 800) to memory 804. Processor 802 may then loadthe instructions from memory 804 to an internal register or internalcache. To execute the instructions, processor 802 may retrieve theinstructions from the internal register or internal cache and decodethem. During or after execution of the instructions, processor 802 maywrite one or more results (which may be intermediate or final results)to the internal register or internal cache. Processor 802 may then writeone or more of those results to memory 804. In particular embodiments,processor 802 executes only instructions in one or more internalregisters or internal caches or in memory 804 (as opposed to storage 806or elsewhere) and operates only on data in one or more internalregisters or internal caches or in memory 804 (as opposed to storage 806or elsewhere). One or more memory buses (which may each include anaddress bus and a data bus) may couple processor 802 to memory 804. Bus812 may include one or more memory buses, as described in further detailbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 802 and memory 804 and facilitateaccesses to memory 804 requested by processor 802. In particularembodiments, memory 804 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate. Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 804 may also includemultiple memory units, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 806 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 806may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage806 may include removable or non-removable (or fixed) media, whereappropriate. Storage 806 may be internal or external to computer system800, where appropriate. In particular embodiments, storage 806 isnon-volatile, solid-state memory. In particular embodiments, storage 806includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 806 taking any suitable physicalform. Storage 806 may include one or more storage control unitsfacilitating communication between processor 802 and storage 806, whereappropriate. Where appropriate, storage 806 may also include multiplestorage units. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 808 includes hardware orsoftware, or both, providing one or more interfaces for communicationbetween computer system 800 and one or more I/O devices. Computer system800 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 800. As an example, and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 808 for them. Where appropriate, I/O interface 808 mayinclude one or more device or software drivers enabling processor 802 todrive one or more of these I/O devices. I/O interface 808 may alsoinclude multiple 1/O interface units, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 810 includes hardwareor software, or both providing one or more interfaces for communication(such as, for example, packet-based communication) between computersystem 800 and one or more other computer systems (or other networkdevices) via one or more networks. As an example, and not by way oflimitation, communication interface 810 may include a network interfacecontroller (NIC) or network adapter for communicating with an Ethernetor any other wire-based network or a wireless NIC (WNIC) or wirelessadapter for communicating with a wireless network, such as a WI-FInetwork. This disclosure contemplates any suitable network and anysuitable communication interface 810 for it. As an example and not byway of limitation, computer system 800 may communicate with an ad hocnetwork, a personal area network (PAN), a local area network (LAN), awide area network (WAN), a metropolitan area network (MAN), or one ormore portions of the Internet or a combination of two or more of these.One or more portions of one or more of these networks may be wired orwireless. As an example, computer system 800 may communicate with awireless PAN (WPAN) (such as, for example, a Bluetooth WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orany other suitable wireless network or a combination of two or more ofthese. Computer system 800 may include any suitable communicationinterface 810 for any of these networks, where appropriate.Communication interface 810 may also include multiple communicationinterface units, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 812 includes hardware or software, orboth coupling components of computer system 800 to each other. As anexample and not by way of limitation, bus 812 may include an AcceleratedGraphics Port (AGP) or any other graphics bus, an Enhanced IndustryStandard Architecture (EISA) bus, a front-side bus (FSB), aHYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture(ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, amemory bus, a Micro Channel Architecture (MCA) bus, a PeripheralComponent Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serialadvanced technology attachment (SATA) bus, a Video Electronics StandardsAssociation local (VLB) bus, or another suitable bus or a combination oftwo or more of these. Bus 812 may also include multiple bus units, whereappropriate. Although this disclosure describes and illustrates aparticular bus, this disclosure contemplates any suitable bus orinterconnect.

A map is a depiction of a whole area or a part of an area whichemphasizes the relationships between elements in space such as objects,landmarks, road signs, road names, or location. In some embodiments, aroad map may display transport links and include points of interest,such as prominent buildings, tourism sites, recreational facilities, andairports. In example embodiments, maps or sections of a map may bedynamic and/or interactive with integration of an automatic or asemi-automatic system. In a semi-automated system, manual input may beused to adjust, correct, or update sections or whole of the map. In someembodiments, the map may be viewed using a user interface and may beshown as a variety of forms such as a topological map in the form of aschematic diagram, a multi-layer map, or a single corrected andsubstantially optimized global map or section of the map.

Image data obtained for processing by at least one image sensor (e.g.,an image sensor attached to a transportation vehicle), in exampleembodiments, may be in the form of a raw image file in order to save,with minimum loss of information, data obtained from the sensor, and theconditions surrounding the capturing of the image, i.e. metadata. Inexample embodiments, in order to convert image metadata into aphotographic rendering of a scene, and then store them as a standardgraphical format, processing may be carried out locally within the imagesensor, or in a raw-file converter, or by using a remote method.Typically, processing image data may include, but not limited to,decoding, defective pixel removal, noise reduction, compression, opticalcorrection, or dynamic range compression.

In embodiments, raw and/or processed image data may be stored within acloud storage which may be accessed through a web service applicationprogramming interface (API) or by applications that utilize the API,such as a cloud desktop storage, a cloud storage gateway, or web-basedcontent management systems. Typically, data may be stored locally orremotely in order to efficiently access data. For image data obtained ofthe real world, decryption keys may be used in order to limit the accessof data and securely store the data obtained by the use of imagesensors.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other types of integratedcircuits (ICs) (such, as for example, field-programmable gate arrays(FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs),hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A or B, or both,” unless expressly indicated otherwise orindicated otherwise by context. Moreover, “and” is both joint andseveral, unless expressly indicated otherwise or indicated otherwise bycontext. Therefore, herein, “A and B” means “A and B, jointly orseverally,” unless expressly indicated otherwise or indicated otherwiseby context.

Methods described herein may vary in accordance with the presentdisclosure. Various embodiments of this disclosure may repeat one ormore steps of the methods described herein, where appropriate. Althoughthis disclosure describes and illustrates particular steps of certainmethods as occurring in a particular order, this disclosure contemplatesany suitable steps of the methods occurring in any suitable order or inany combination which may include all, some, or none of the steps of themethods. Furthermore, although this disclosure may describe andillustrate particular components, devices, or systems carrying outparticular steps of a method, this disclosure contemplates any suitablecombination of any suitable components, devices, or systems carrying outany suitable steps of the method.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, modules,elements, feature, functions, operations, or steps, any of theseembodiments may include any combination or permutation of any of thecomponents, modules, elements, features, functions, operations, or stepsdescribed or illustrated anywhere herein that a person having ordinaryskill in the art would comprehend. Furthermore, reference in theappended claims to an apparatus or system or a component of an apparatusor system being adapted to, arranged to, capable of, configured to,enabled to, operable to, or operative to perform a particular functionencompasses that apparatus, system, component, whether or not it or thatparticular function is activated, turned on, or unlocked, as long asthat apparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

Many variations to the example method are possible. It should beappreciated that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments discussed herein unless otherwisestated.

Any system features as described herein may also be provided as a methodfeature, and vice versa. As used herein, means plus function featuresmay be expressed alternatively in terms of their correspondingstructure.

Any feature in one aspect may be applied to other aspects, in anyappropriate combination. In particular, method aspects may be applied tosystem aspects, and vice versa. Furthermore, any, some and/or allfeatures in one aspect can be applied to any, some and/or all featuresin any other aspect, in any appropriate combination.

It should also be appreciated that particular combinations of thevarious features described and defined in any aspects can be implementedand/or supplied and/or used independently.

What is claimed is:
 1. A method comprising; obtaining a first layer ofmap data associated with sensor data capturing a geographical area, thefirst layer of map data comprising an aggregated overhead-view image ofthe geographical area, wherein the aggregated overhead-view image isgenerated from aggregated pixel values from a plurality of imagesassociated with the geographical area; obtaining a second layer of mapdata, the second layer of map data comprising label data for thegeographical area derived from the aggregated overhead-view image of thegeographical area; and causing the first layer of map data and thesecond layer of map data to be presented to a user for curation of thelabel data.
 2. The method as recited in claim 1, further comprising:generating the aggregated overhead-view image of the geographical areausing a ground map of the geographical area and a plurality of images ofthe geographical area.
 3. The method as recited in claim 2, whereingenerating the aggregated overhead-view image further comprises:determining a color for each of the aggregated pixel values.
 4. Themethod as recited in claim 1, wherein curation of the label datacomprises one or more of: verifying the label data, editing the labeldata, adding new label data to the label data, or removing incorrect orirrelevant label data from the label data based on the correspondence ofthe aggregated overhead-view image to the label data.
 5. The method asrecited in claim 1, wherein the second layer of the map data isgenerated by automatically extracting label data from the aggregatedoverhead-view image of the geographical area, wherein the label datacomprises semantic map data.
 6. The method as recited in claim 1,wherein the label data is automatically generated using one or more of:machine learning models; classifiers; or Generative AdversarialNetworks.
 7. The method as recited in claim 1, wherein the label datacomprises one or more of: lane boundaries; lane connectivity; speedlimits; types of traffic elements; crosswalks; speed bumps; pedestrianpaths or sidewalks; manhole covers; or curbs.
 8. A method comprising:receiving labeled map data of a geographical area from a mapping system,the labeled map data including: a first layer of map data comprising anaggregated overhead-view image of a geographical area, wherein theaggregated overhead-view image is generated from aggregated pixel valuesfrom a plurality of images of the geographical area; and a second layerof map data comprising label data for the geographical area derived fromthe aggregated overhead-view image of the geographical area; displayingthe first layer of map data and the second layer of map data to a user;receiving user input comprising one or more adjustments to the labeldata; and causing the label data to be adjusted in accordance with theuser input.
 9. The method as recited in claim 8, wherein causing thelabel data to be adjusted in accordance with the user input comprisesone of (i) adjusting the label data locally and then providing theadjusted label data to the mapping system or (ii) providing the userinput to the mapping system and thereby causing mapping system to adjustthe label data.
 10. The method as recited in claim 8, wherein causingthe label data to be adjusted in accordance with the user inputcomprises causing the second layer of map data to be updated.
 11. Themethod as recited in claim 8, further comprising: updating the displayedsecond layer of the map in accordance with the user input.
 12. Themethod as recited in claim 8, wherein the one or more adjustments to thelabel data are based on one or more of: a set of guidelines; a set ofinstructions; one or more plug-ins for adjustment; or one or more toolsfor adjustment input.
 13. The method as recited in claim 8, wherein theone or more adjustments of the label data comprise one or more of:visual manipulation; determining abnormalities; determiningalignments/misalignments; inputting one or more annotations;selecting/de-selecting one or more of the label data;removing/re-embedding one or more of the label data; hiding/exposing oneor more of the label data; or enlargement/diminution of one or more ofthe label data.
 14. The method as recited in claim 8, wherein the maplayers are stored in one or more local system or a remote system. 15.The method as recited in claim 8, further comprising: causing a globalmap to be updated in accordance with the user input.
 16. The method asrecited in claim 8, wherein displaying the first layer of map data andthe second layer of map data comprises displaying the second layer ofthe map overlaid on the first layer of the map.
 17. A computer systemcomprising: at least one processor; at least one non-transitorycomputer-readable medium; program instructions stored on the at leastone non-transitory computer-readable medium that are executable by theat least one processor such that the computer system is capable of:receiving labeled map data of a geographical area from a mapping system,the labeled map data including: a first layer of map data comprising anaggregated overhead-view image of a geographical area, wherein theaggregated overhead-view image is generated from aggregated pixel valuesfrom a plurality of images of the geographical area; and a second layerof map data comprising label data for the geographical area derived fromthe aggregated overhead-view image of the geographical area; displayingthe first layer of map data and the second layer of map data to a user;receiving user input comprising one or more adjustments to the labeldata; and causing the label data to be adjusted in accordance with theuser input.
 18. The computer system of claim 17, wherein causing thelabel data to be adjusted in accordance with the user input comprisesone of (i) adjusting the label data locally and then providing theadjusted label data to the mapping system or (ii) providing the userinput to the mapping system and thereby causing mapping system to adjustthe label data.
 19. The computer system of claim 17, further comprisingprogram instructions that are executable by the at least one processorsuch that the computer system is capable of: updating the displayedsecond layer of the map in accordance with the user input.
 20. Thecomputer system of claim 17, wherein displaying the first layer of mapdata and the second layer of map data comprises displaying the secondlayer of the map overlaid on the first layer of the map.